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Xie H, Wu H, Wang J, Mendieta JB, Yu H, Xiang Y, Anbananthan H, Zhang J, Zhao H, Zhu Z, Huang Q, Fang R, Zhu C, Li Z. Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107975. [PMID: 38128464 DOI: 10.1016/j.cmpb.2023.107975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/08/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment. METHODS The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data. RESULTS The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points. CONCLUSIONS Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation.
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Affiliation(s)
- Hujin Xie
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia.
| | - Hao Wu
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Jiaqiu Wang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; School of Engineering, London South Bank University, London, UK
| | - Jessica Benitez Mendieta
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Han Yu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Yuqiao Xiang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Haveena Anbananthan
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Jianjian Zhang
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, China
| | - Huilin Zhao
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, China
| | - Zhengduo Zhu
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Qiuxiang Huang
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Runxing Fang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Chengcheng Zhu
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States
| | - Zhiyong Li
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD 4000, Australia; Faculty of Sports Science, Ningbo University, Ningbo, Zhejiang 315211, China.
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Mezali F, Naima K, Benmamar S, Liazid A. Study and modeling of the thrombosis of small cerebral aneurysms, with and without flow diverter, by the lattice Boltzmann method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107456. [PMID: 36924532 DOI: 10.1016/j.cmpb.2023.107456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Small cerebral aneurysms are currently commonly treated non-invasively by flow diverter device. These stents lead to thrombotic occlusion of the aneurysm soon after their placement. The purpose of this work is to model clotting into intracranial aneurysms with and without stents, using a non-Newtonian of blood behavior, and to investigate the importance of stent to generate desired thrombus in intracranial aneurysms. METHOD The description of blood flow is made by the Boltzmann lattice equations, while thrombosis is modeled by the "fluid age" model. The lattice Boltzmann method is a computational technique for simulating fluid dynamics. The method is based on a mesoscopic approach, where the fluid is represented by a set of particles that move and interact on a grid. The model for blood coagulation is described by lattice Boltzmann Method, and it doesn't take into account the complicated coagulation pathway, this main idea is developed using the model of residence time of blood: all fluid in the domain is assumed to be capable of clotting, given enough time. The fluid age is measured by a passive scalar using a transport equation, and the node coagulates if the fluid age increases enough. Three small aneurysms of different sizes and shapes with three stents of various porosities were used to test the ability of the model to predict thrombosis. The "occlusion rate" parameter is used to assess the effectiveness of the flow diverter device. RESULTS For the large aspect ratio factor, the occlusion is: 91% for flow diverter devise with seven struts. For medium aspect ratio, a rate of 80% is achieved. An occlusion rate of slightly more than 30% is obtained for very small aneurysms with low aspect ratio. The Newtonian model underestimates the volume of thrombosis generated. The difference in the prediction of the thrombosis volume between the Newtonian and no-Newtonian Carreau-Yasuda models is approximately 10%. CONCLUSION The occlusion rate is proportional to the aspect ratio form factor. For the large and medium aspect ratio factors, the occlusion is satisfactory. Concerning very small aneurysms with low aspect ratio, aneurysm occlusion is low. This rate can be improved to almost complete occlusion if the flow diverter device is doubled. The generality of the model suggests its extensibility toward any other type of thrombosis (stenosis, thrombosis in aortic aneurysms).
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Affiliation(s)
- Farouk Mezali
- Water Sciences Research laboratory: LRS-Eau, National Polytechnic School, El Harrach, Algiers; Hydraulics department, Faculty of Technology, BP 166, M'sila 28000, Algeria
| | - Khatir Naima
- Department of Technology, University Centre of Naama (Ctr Univ Naama), P.O. Box 66, Naama 45000, Algeria.
| | - Saida Benmamar
- Water Sciences Research laboratory: LRS-Eau, National Polytechnic School, El Harrach, Algiers
| | - Abdelkrim Liazid
- Departement of physics, Faculty of Technology Faculty, Abou Bekr Belkaid University, 22 Rue Abi Ayed Abdelkrim, Tlemcen 13000, Algeria
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